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author de Souza, Flávio Henrique Batista
Couto, Braulio Roberto Gonçalves Marinho
da Conceição, Felipe Leandro Andrade
da Silva, Gabriel Henrique Silvestre
Dias, Igor Gonçalves
Rigueira, Rafael Vieira Magno
Pimenta, Gustavo Maciel
Martins, Maurilio B
Mendes, Júlio César O
Januário, Guilherme Brangioni
Oliveira, Rayane Thamires
de Vasconcelos, Laura Ferraz
de Araújo, Laís L
Rodrigues, Ana Clara Resende
Silva, Camila Morais Oliveira E
De Souza, Eduarda Viana
Melo, Júlia Faria
De Sá, Maria Cláudia Assunção
Silva, Walquíria Magalhães
Alves, Beatriz Izabel de Souza
Resende, Débora Mendes
Borges A., Guilherme Prata
Santos, Gustavo Ryan Martins
Xavier, João Paulo Silva Santos
Lacerda, Lucas de Paula
Silva, Maria Júlia Santos e
author_facet de Souza, Flávio Henrique Batista
Couto, Braulio Roberto Gonçalves Marinho
da Conceição, Felipe Leandro Andrade
da Silva, Gabriel Henrique Silvestre
Dias, Igor Gonçalves
Rigueira, Rafael Vieira Magno
Pimenta, Gustavo Maciel
Martins, Maurilio B
Mendes, Júlio César O
Januário, Guilherme Brangioni
Oliveira, Rayane Thamires
de Vasconcelos, Laura Ferraz
de Araújo, Laís L
Rodrigues, Ana Clara Resende
Silva, Camila Morais Oliveira E
De Souza, Eduarda Viana
Melo, Júlia Faria
De Sá, Maria Cláudia Assunção
Silva, Walquíria Magalhães
Alves, Beatriz Izabel de Souza
Resende, Débora Mendes
Borges A., Guilherme Prata
Santos, Gustavo Ryan Martins
Xavier, João Paulo Silva Santos
Lacerda, Lucas de Paula
Silva, Maria Júlia Santos e
author_sort de Souza, Flávio Henrique Batista
collection PubMed
description BACKGROUND: A research was conducted between July 2016 and June 2018 in five hospitals in Belo Horizonte, a city of 3,000,000 inhabitants, focused on surgical site infection (SSI) in patients undergoing limb amputation surgery procedure. The main objective is to statistically evaluate such incidences and enable a study of the prediction power of SSI through MLPs (Multilayer Perceptron), a pattern recognition algorithm. METHODS: Data were collected on SSI by the Hospital Infection Control Committees (CCIH) of the hospitals involved. The information was forwarded to the NOIS (Nosocomial Infection Study) Project. After data collection, three procedures were performed: a treatment of the database collected for the use of intact samples; a statistical analysis on the profile of the hospitals collected and; an assessment of the predictive power of five types of MLP (Backpropagation Standard, Momentum, Resilient Propagation, Weight Decay, and Quick Propagation) for SSI prediction. MLPs were tested with 3, 5, 7, and 10 hidden layer neurons and a database split for the resampling process (65% or 75% for testing, 35% or 25% for validation). They were compared by measuring AUC (Area Under the Curve - ranging from 0 to 1) presented for each of the configurations. RESULTS: From 969 data, only 507 were intact for analysis. Statistically: in 12.45% there was an incidence of global infection and that in 10.67% of the cases were SSI (among which, 94.6% had to be hospitalized for more than 10 days); patients were hospitalized on average 21 days (from 0 to 141 days); the average duration is 78 minutes (maximum 360 minutes); 53 deaths (a 16.98% death rate in case of SSI). A maximum prediction power of 0.688 was found. CONCLUSION: Despite the loss rate of almost 40% of the database samples due to the presence of noise, it was obtained a relevant sampling to evaluate the profile the hospitals. For the predictive process, although some configurations reached 0.688, which makes promising the use of the automated SSI monitoring framework for patients undergoing limb amputation surgery. To optimize data collection and enable other hospitals to use the SSI prediction tool (available in www.sacihweb.com), two mobile application were developed: one for monitoring the patient in the hospital and the other for post-hospital discharge monitoring. DISCLOSURES: All Authors: No reported disclosures
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spelling pubmed-77760742021-01-07 893. Occurrence’s Prediction of Surgical Site Infection in Limb Amputation Surgery de Souza, Flávio Henrique Batista Couto, Braulio Roberto Gonçalves Marinho da Conceição, Felipe Leandro Andrade da Silva, Gabriel Henrique Silvestre Dias, Igor Gonçalves Rigueira, Rafael Vieira Magno Pimenta, Gustavo Maciel Martins, Maurilio B Mendes, Júlio César O Januário, Guilherme Brangioni Oliveira, Rayane Thamires de Vasconcelos, Laura Ferraz de Araújo, Laís L Rodrigues, Ana Clara Resende Silva, Camila Morais Oliveira E De Souza, Eduarda Viana Melo, Júlia Faria De Sá, Maria Cláudia Assunção Silva, Walquíria Magalhães Alves, Beatriz Izabel de Souza Resende, Débora Mendes Borges A., Guilherme Prata Santos, Gustavo Ryan Martins Xavier, João Paulo Silva Santos Lacerda, Lucas de Paula Silva, Maria Júlia Santos e Open Forum Infect Dis Poster Abstracts BACKGROUND: A research was conducted between July 2016 and June 2018 in five hospitals in Belo Horizonte, a city of 3,000,000 inhabitants, focused on surgical site infection (SSI) in patients undergoing limb amputation surgery procedure. The main objective is to statistically evaluate such incidences and enable a study of the prediction power of SSI through MLPs (Multilayer Perceptron), a pattern recognition algorithm. METHODS: Data were collected on SSI by the Hospital Infection Control Committees (CCIH) of the hospitals involved. The information was forwarded to the NOIS (Nosocomial Infection Study) Project. After data collection, three procedures were performed: a treatment of the database collected for the use of intact samples; a statistical analysis on the profile of the hospitals collected and; an assessment of the predictive power of five types of MLP (Backpropagation Standard, Momentum, Resilient Propagation, Weight Decay, and Quick Propagation) for SSI prediction. MLPs were tested with 3, 5, 7, and 10 hidden layer neurons and a database split for the resampling process (65% or 75% for testing, 35% or 25% for validation). They were compared by measuring AUC (Area Under the Curve - ranging from 0 to 1) presented for each of the configurations. RESULTS: From 969 data, only 507 were intact for analysis. Statistically: in 12.45% there was an incidence of global infection and that in 10.67% of the cases were SSI (among which, 94.6% had to be hospitalized for more than 10 days); patients were hospitalized on average 21 days (from 0 to 141 days); the average duration is 78 minutes (maximum 360 minutes); 53 deaths (a 16.98% death rate in case of SSI). A maximum prediction power of 0.688 was found. CONCLUSION: Despite the loss rate of almost 40% of the database samples due to the presence of noise, it was obtained a relevant sampling to evaluate the profile the hospitals. For the predictive process, although some configurations reached 0.688, which makes promising the use of the automated SSI monitoring framework for patients undergoing limb amputation surgery. To optimize data collection and enable other hospitals to use the SSI prediction tool (available in www.sacihweb.com), two mobile application were developed: one for monitoring the patient in the hospital and the other for post-hospital discharge monitoring. DISCLOSURES: All Authors: No reported disclosures Oxford University Press 2020-12-31 /pmc/articles/PMC7776074/ http://dx.doi.org/10.1093/ofid/ofaa439.1081 Text en © The Author 2020. Published by Oxford University Press on behalf of Infectious Diseases Society of America. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Poster Abstracts
de Souza, Flávio Henrique Batista
Couto, Braulio Roberto Gonçalves Marinho
da Conceição, Felipe Leandro Andrade
da Silva, Gabriel Henrique Silvestre
Dias, Igor Gonçalves
Rigueira, Rafael Vieira Magno
Pimenta, Gustavo Maciel
Martins, Maurilio B
Mendes, Júlio César O
Januário, Guilherme Brangioni
Oliveira, Rayane Thamires
de Vasconcelos, Laura Ferraz
de Araújo, Laís L
Rodrigues, Ana Clara Resende
Silva, Camila Morais Oliveira E
De Souza, Eduarda Viana
Melo, Júlia Faria
De Sá, Maria Cláudia Assunção
Silva, Walquíria Magalhães
Alves, Beatriz Izabel de Souza
Resende, Débora Mendes
Borges A., Guilherme Prata
Santos, Gustavo Ryan Martins
Xavier, João Paulo Silva Santos
Lacerda, Lucas de Paula
Silva, Maria Júlia Santos e
893. Occurrence’s Prediction of Surgical Site Infection in Limb Amputation Surgery
title 893. Occurrence’s Prediction of Surgical Site Infection in Limb Amputation Surgery
title_full 893. Occurrence’s Prediction of Surgical Site Infection in Limb Amputation Surgery
title_fullStr 893. Occurrence’s Prediction of Surgical Site Infection in Limb Amputation Surgery
title_full_unstemmed 893. Occurrence’s Prediction of Surgical Site Infection in Limb Amputation Surgery
title_short 893. Occurrence’s Prediction of Surgical Site Infection in Limb Amputation Surgery
title_sort 893. occurrence’s prediction of surgical site infection in limb amputation surgery
topic Poster Abstracts
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7776074/
http://dx.doi.org/10.1093/ofid/ofaa439.1081
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